Before you look up a competitor's revenue, write down the decision it is meant to inform. "Is this niche big enough to enter?" "Is the leader ten times our size or a hundred times?" "Did their price change move anything?" Those questions can be answered from free, public data. "What is their profit?" and "What is their exact MRR?" cannot, and no free tool will change that.

This guide covers what you can observe for free, how to turn it into a modeled estimate, and where the numbers stop being useful.

What you can see without paying

The exact figures belong to the competitor. Apple reports units and proceeds to the developer in App Store Connect (View units, proceeds, sales, and pre-orders), and Google shows Play developers their own Statistics page (View app statistics). You will not get either for someone else's app.

What is public is the storefront itself:

  • Chart positions. Top free, top paid and top grossing, overall and by category, for each store and country. Grossing position is the public signal most directly tied to spending.
  • Rating counts over time. A rating count that grows while an app holds a high chart position fits the rank signal. A count that stays flat for weeks while the app sits near the top of a downloads chart is a reason to doubt the rank.
  • The listing. Price, whether the app offers in-app purchases, category and update history tell you how the app makes money, which tells you whether a grossing-chart estimate can even see it.

If the competitor's parent company is publicly listed, its filings are a primary source worth checking. The SEC's EDGAR full-text search lets you search filings by company, though a filing usually reports a portfolio or segment, not one app.

Turning a chart position into a rough number by hand

You can do a back-of-envelope version yourself with published anchors. Distimo reported that in May 2013 a US iPhone app needed about $47,000 a day in revenue to break into the top 10 grossing, and about $12,000 for the top 50 (TechCrunch). For downloads, Sensor Tower reported that a non-game iPhone app needed a median of 52,000 daily US downloads to reach the top 10 in 2022, and 29,000 on Google Play (Sensor Tower).

That gets you an order of magnitude for apps near those positions, in the US, in the years the anchors describe. Past that, the method breaks quickly. The anchors are old vendor estimates. They describe the overall chart, not a category chart. They do not tell you what rank 140 is worth. And they are US-only, apart from a few relative figures such as Distimo's finding that the UK needed "only 21 percent" of the US downloads for the top 25 (TechCrunch). A by-hand number also hides all of that, because it comes out as a single figure.

A model does the same arithmetic more carefully: it fits a curve per store and chart, converts category positions to overall equivalents, averages a month of daily positions and reports the spread the anchors allow. We explain each step in how app revenue estimates are made from chart ranks, and why they come as ranges.

A free workflow in ASOClimb

ASOClimb's Revenue Estimates page does this for every app that appears in the public charts it captures, and you can open it with a free account. Here is the sequence we would follow for one competitor:

  1. Pick the store and country first. Estimates are per storefront. The United States is the only storefront with absolute calibration anchors, so it is the only one where Medium confidence is possible for both metrics. Great Britain and Japan can reach Medium for downloads only. Elsewhere, expect Low.
  2. Search by name, App Store id or package name. If the app has not appeared in a captured chart in the last 30 days, there is no estimate for it, and ASOClimb shows no row rather than a zero.
  3. Open the app and read the confidence reasons before the figures. The detail view lists every chart position the estimate used, which chart and category each came from, and why the confidence is what it is: too few charted days, a category-only position, a storefront without an anchor.
  4. Check the revenue basis. An app needs at least three days in the grossing chart to get a revenue point estimate. With fewer days you get bounds only, and an app never seen in a captured grossing chart gets only an upper bound.
  5. Look at the trend, not one month. The detail view charts weekly history as a band, and change against the prior period reads "~flat (within estimate range)" when the two ranges overlap heavily.
  6. Add it as a competitor of your app if you will check it again. Competitors you add appear in the revenue section of your dashboard beside your own apps, and you can export the current page of the table to CSV with low, point and high values and the confidence for each.

If you connect App Store Connect, your own app's reported figures appear beside its estimate, never merged into it, which is a sanity check on the model in your own niche. That comparison covers App Store listings only, because reported figures are read from App Store Connect.

What the numbers can tell you

  • Order of magnitude. Whether a competitor is roughly a $5K-a-month app or a $500K-a-month app is usually clear, even at Low confidence.
  • Whether two apps can be told apart. If their ranges do not overlap, the ordering is meaningful. If they overlap, it is not.
  • Direction over several months. A range that keeps moving up, clear of last quarter's range, is real movement. One month's shift inside the overlap is not.
  • Niche shape. A handful of apps with high ranges and a long tail of bounds-only apps tells you how concentrated a category is.

What they cannot tell you

  • Ad revenue and web payments. Grossing charts see store purchases only. An app that sells subscriptions on its website or earns mostly from ads will look smaller than it is, and nothing in the chart data can correct for that.
  • Profit, margins or lifetime value. Gross revenue is not what the business keeps after the store commission and after paying for ads, staff and servers.
  • Which commission tier applies. Apple's Small Business Program charges 15% (Apple), and Google lists several US rate tiers (Play Console Help). ASOClimb shows gross revenue, what users paid before that commission, so no tier is assumed; what the developer receives is lower by whichever tier applies.
  • Paid-app revenue from price. Chart rows carry no price, so ASOClimb does not compute price times downloads.
  • Anything about an app that never charts. Below the charts there is no rank to model from.
  • Precision outside the US. Most storefronts have no public anchor, so their ranges are wide by design.

A hypothetical comparison

For illustration only. These are not observed or modeled figures for any real app. You are weighing a habit-tracker niche in the US App Store. Competitor A shows "$60K to $250K per month, Medium confidence", based on daily overall-chart positions. Competitor B shows "$8K to $120K, Low confidence", based on a category chart and nine grossing days. The ranges overlap, so you cannot say B is smaller than A. You can say the niche supports at least one app in the tens of thousands a month, and you can choose to watch B for a month before deciding.

Record what you looked at the way you would record a keyword rank: store, country, date and model version, as covered in keyword rank tracking without fooling yourself. A screenshot of a figure without its scope is how a modeled number ends up quoted as fact.

Try it

The methodology page lists every anchor the model uses and states that ASOClimb has published no backtest against reported revenue, so it makes no accuracy claim. When you are ready to look up a competitor, sign in and open Revenue Estimates.